Overview

Dataset statistics

Number of variables10
Number of observations442
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory80.3 B

Variable types

Numeric9
Categorical1

Alerts

s1 is highly overall correlated with s2 and 2 other fieldsHigh correlation
s2 is highly overall correlated with s1 and 1 other fieldsHigh correlation
s3 is highly overall correlated with s4High correlation
s4 is highly overall correlated with s1 and 3 other fieldsHigh correlation
s5 is highly overall correlated with s1 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-01-22 06:51:56.051433
Analysis finished2023-01-22 06:52:19.186378
Duration23.13 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct58
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6389946 × 10-16
Minimum-0.10722563
Maximum0.11072668
Zeros0
Zeros (%)0.0%
Negative202
Negative (%)45.7%
Memory size3.6 KiB
2023-01-22T12:22:19.706135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.10722563
5-th percentile-0.085430401
Q1-0.037299266
median0.0053830604
Q30.038075906
95-th percentile0.070768752
Maximum0.11072668
Range0.21795231
Interquartile range (IQR)0.075375173

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-1.308577 × 1014
Kurtosis-0.67122369
Mean-3.6389946 × 10-16
Median Absolute Deviation (MAD)0.036325385
Skewness-0.23138153
Sum-1.6076029 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:19.955451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01628067573 19
 
4.3%
0.04170844488 17
 
3.8%
0.009015598825 16
 
3.6%
-0.02730978568 15
 
3.4%
-0.001882016528 14
 
3.2%
-0.05273755484 14
 
3.2%
0.04534098334 14
 
3.2%
0.01264813728 14
 
3.2%
0.06713621404 13
 
2.9%
0.005383060374 13
 
2.9%
Other values (48) 293
66.3%
ValueCountFrequency (%)
-0.1072256316 3
 
0.7%
-0.1035930932 3
 
0.7%
-0.09996055471 2
 
0.5%
-0.09632801625 4
0.9%
-0.0926954778 4
0.9%
-0.08906293935 3
 
0.7%
-0.0854304009 5
1.1%
-0.08179786245 2
 
0.5%
-0.078165324 4
0.9%
-0.07453278555 8
1.8%
ValueCountFrequency (%)
0.1107266755 2
 
0.5%
0.09619652165 2
 
0.5%
0.0925639832 1
 
0.2%
0.08893144475 1
 
0.2%
0.0852989063 1
 
0.2%
0.08166636785 5
 
1.1%
0.07803382939 1
 
0.2%
0.07440129094 6
1.4%
0.07076875249 7
1.6%
0.06713621404 13
2.9%

sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
-0.044641636506989
235 
0.0506801187398187
207 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters7956
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0506801187398187
2nd row-0.044641636506989
3rd row0.0506801187398187
4th row-0.044641636506989
5th row-0.044641636506989

Common Values

ValueCountFrequency (%)
-0.044641636506989 235
53.2%
0.0506801187398187 207
46.8%

Length

2023-01-22T12:22:20.205975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-22T12:22:20.488480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.044641636506989 235
53.2%
0.0506801187398187 207
46.8%

Most occurring characters

ValueCountFrequency (%)
0 1533
19.3%
6 1147
14.4%
8 1063
13.4%
1 856
10.8%
4 705
8.9%
9 677
8.5%
. 442
 
5.6%
3 442
 
5.6%
5 442
 
5.6%
7 414
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7279
91.5%
Other Punctuation 442
 
5.6%
Dash Punctuation 235
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1533
21.1%
6 1147
15.8%
8 1063
14.6%
1 856
11.8%
4 705
9.7%
9 677
9.3%
3 442
 
6.1%
5 442
 
6.1%
7 414
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1533
19.3%
6 1147
14.4%
8 1063
13.4%
1 856
10.8%
4 705
8.9%
9 677
8.5%
. 442
 
5.6%
3 442
 
5.6%
5 442
 
5.6%
7 414
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1533
19.3%
6 1147
14.4%
8 1063
13.4%
1 856
10.8%
4 705
8.9%
9 677
8.5%
. 442
 
5.6%
3 442
 
5.6%
5 442
 
5.6%
7 414
 
5.2%

bmi
Real number (ℝ)

Distinct163
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.0227429 × 10-16
Minimum-0.090275296
Maximum0.17055523
Zeros0
Zeros (%)0.0%
Negative247
Negative (%)55.9%
Memory size3.6 KiB
2023-01-22T12:22:20.787987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.090275296
5-th percentile-0.06656343
Q1-0.034229068
median-0.0072837662
Q30.031248015
95-th percentile0.085408072
Maximum0.17055523
Range0.26083052
Interquartile range (IQR)0.065477083

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-5.9355072 × 1013
Kurtosis0.095094474
Mean-8.0227429 × 10-16
Median Absolute Deviation (MAD)0.03125655
Skewness0.59814849
Sum-3.5504932 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:21.120869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.02452875939 8
 
1.8%
-0.03099563184 8
 
1.8%
-0.008361578284 7
 
1.6%
-0.04608500087 7
 
1.6%
-0.02560657147 7
 
1.6%
0.001338730381 6
 
1.4%
0.004572166603 6
 
1.4%
0.01427247527 6
 
1.4%
-0.0202175111 6
 
1.4%
-0.02345094732 6
 
1.4%
Other values (153) 375
84.8%
ValueCountFrequency (%)
-0.0902752959 1
0.2%
-0.08919748382 1
0.2%
-0.08488623553 1
0.2%
-0.08380842346 1
0.2%
-0.08165279931 2
0.5%
-0.08057498723 1
0.2%
-0.07949717516 1
0.2%
-0.07734155101 2
0.5%
-0.07626373894 1
0.2%
-0.07518592686 1
0.2%
ValueCountFrequency (%)
0.170555226 1
0.2%
0.1608549173 1
0.2%
0.1371430517 1
0.2%
0.1285205551 1
0.2%
0.127442743 1
0.2%
0.1252871189 1
0.2%
0.1231314947 1
0.2%
0.1145089981 1
0.2%
0.1112755619 1
0.2%
0.1101977498 1
0.2%

bp
Real number (ℝ)

Distinct100
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2798492 × 10-16
Minimum-0.1123996
Maximum0.13204422
Zeros0
Zeros (%)0.0%
Negative244
Negative (%)55.2%
Memory size3.6 KiB
2023-01-22T12:22:21.421476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.1123996
5-th percentile-0.074355881
Q1-0.036656447
median-0.0056706106
Q30.035643838
95-th percentile0.083671884
Maximum0.13204422
Range0.24444382
Interquartile range (IQR)0.072300285

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)3.7206766 × 1014
Kurtosis-0.53277972
Mean1.2798492 × 10-16
Median Absolute Deviation (MAD)0.034428707
Skewness0.29066385
Sum5.6843419 × 10-14
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:21.771594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.04009931749 21
 
4.8%
-0.005670610555 21
 
4.8%
-0.02632783472 20
 
4.5%
0.02187235499 15
 
3.4%
-0.0332135761 14
 
3.2%
-0.02288496402 13
 
2.9%
-0.01599922264 11
 
2.5%
0.00810087222 11
 
2.5%
-0.01255635194 11
 
2.5%
0.04941532054 11
 
2.5%
Other values (90) 294
66.5%
ValueCountFrequency (%)
-0.1123996021 1
 
0.2%
-0.1089567314 1
 
0.2%
-0.10207099 1
 
0.2%
-0.1009233664 1
 
0.2%
-0.09862811929 1
 
0.2%
-0.08485663651 4
0.9%
-0.08141376582 4
0.9%
-0.07797089512 1
 
0.2%
-0.07452802443 9
2.0%
-0.07108515374 1
 
0.2%
ValueCountFrequency (%)
0.1320442172 1
 
0.2%
0.1251584758 1
 
0.2%
0.1079441223 3
0.7%
0.1045012516 2
 
0.5%
0.101058381 1
 
0.2%
0.09876313371 1
 
0.2%
0.09761551026 5
1.1%
0.09417263956 1
 
0.2%
0.09072976887 2
 
0.5%
0.08728689818 4
0.9%

s1
Real number (ℝ)

Distinct141
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.0425405 × 10-17
Minimum-0.12678067
Maximum0.15391371
Zeros0
Zeros (%)0.0%
Negative240
Negative (%)54.3%
Memory size3.6 KiB
2023-01-22T12:22:22.087999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.12678067
5-th percentile-0.073118508
Q1-0.03424784
median-0.0043208655
Q30.028358015
95-th percentile0.08367132
Maximum0.15391371
Range0.28069438
Interquartile range (IQR)0.062605855

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-5.2661138 × 1014
Kurtosis0.2329479
Mean-9.0425405 × 10-17
Median Absolute Deviation (MAD)0.030958939
Skewness0.37810821
Sum-3.9412917 × 10-14
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:22.403886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.007072771253 10
 
2.3%
-0.03734373413 10
 
2.3%
0.01219056876 9
 
2.0%
0.02044628591 9
 
2.0%
0.001182945896 8
 
1.8%
0.02457414449 8
 
1.8%
-0.02496015841 8
 
1.8%
-0.004320865537 8
 
1.8%
-0.002944912678 8
 
1.8%
-0.009824676969 7
 
1.6%
Other values (131) 357
80.8%
ValueCountFrequency (%)
-0.1267806699 1
0.2%
-0.1088932828 1
0.2%
-0.1047654242 1
0.2%
-0.1033894713 1
0.2%
-0.1006375656 1
0.2%
-0.09650970704 2
0.5%
-0.0910058956 1
0.2%
-0.08962994275 2
0.5%
-0.08825398989 1
0.2%
-0.08687803703 1
0.2%
ValueCountFrequency (%)
0.1539137132 1
0.2%
0.1525377603 1
0.2%
0.1332744203 1
0.2%
0.1277706089 2
0.5%
0.126394656 1
0.2%
0.1250187031 2
0.5%
0.1195148917 1
0.2%
0.1098832217 2
0.5%
0.1030034574 1
0.2%
0.09887559883 1
0.2%

s2
Real number (ℝ)

Distinct302
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.303005 × 10-16
Minimum-0.11561307
Maximum0.19878799
Zeros0
Zeros (%)0.0%
Negative239
Negative (%)54.1%
Memory size3.6 KiB
2023-01-22T12:22:22.754181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.11561307
5-th percentile-0.072711727
Q1-0.030358397
median-0.0038190651
Q30.029844395
95-th percentile0.079462768
Maximum0.19878799
Range0.31440106
Interquartile range (IQR)0.060202792

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)3.6545561 × 1014
Kurtosis0.60138115
Mean1.303005 × 10-16
Median Absolute Deviation (MAD)0.029905678
Skewness0.4365918
Sum5.7287508 × 10-14
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:23.057119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.001000728964 5
 
1.1%
0.01622243643 5
 
1.1%
0.056618588 4
 
0.9%
-0.02480001206 4
 
0.9%
-0.04703355285 4
 
0.9%
-0.0138398159 4
 
0.9%
-0.05454911593 3
 
0.7%
-0.02166852744 3
 
0.7%
0.004635943348 3
 
0.7%
0.03751653184 3
 
0.7%
Other values (292) 404
91.4%
ValueCountFrequency (%)
-0.115613066 1
0.2%
-0.1127947298 1
0.2%
-0.106844909 1
0.2%
-0.1043397214 1
0.2%
-0.1008950883 1
0.2%
-0.09713730673 1
0.2%
-0.09619786135 1
0.2%
-0.09588471289 1
0.2%
-0.09463211904 1
0.2%
-0.09056118904 1
0.2%
ValueCountFrequency (%)
0.1987879897 1
0.2%
0.1558866504 1
0.2%
0.1314610704 1
0.2%
0.1302084765 1
0.2%
0.1280164373 1
0.2%
0.1273901404 1
0.2%
0.1251981011 1
0.2%
0.1170562411 1
0.2%
0.1164299442 1
0.2%
0.1089143811 1
0.2%

s3
Real number (ℝ)

Distinct63
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.5583195 × 10-16
Minimum-0.10230705
Maximum0.18117906
Zeros0
Zeros (%)0.0%
Negative243
Negative (%)55.0%
Memory size3.6 KiB
2023-01-22T12:22:23.403826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.10230705
5-th percentile-0.065490672
Q1-0.035117161
median-0.0065844676
Q30.029311501
95-th percentile0.07790912
Maximum0.18117906
Range0.28348611
Interquartile range (IQR)0.064428662

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-1.0446623 × 1014
Kurtosis0.98150746
Mean-4.5583195 × 10-16
Median Absolute Deviation (MAD)0.031293921
Skewness0.79925512
Sum-2.0150548 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:23.770211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.01394774322 22
 
5.0%
-0.04340084565 19
 
4.3%
-0.03971920785 18
 
4.1%
-0.002902829807 15
 
3.4%
-0.03235593224 15
 
3.4%
-0.02131101883 15
 
3.4%
0.008142083605 15
 
3.4%
-0.02867429444 15
 
3.4%
-0.006584467611 14
 
3.2%
0.01550535921 14
 
3.2%
Other values (53) 280
63.3%
ValueCountFrequency (%)
-0.1023070505 1
 
0.2%
-0.09862541271 1
 
0.2%
-0.09126213711 1
 
0.2%
-0.08021722369 2
 
0.5%
-0.07653558589 5
1.1%
-0.07285394808 5
1.1%
-0.06917231028 7
1.6%
-0.06549067248 6
1.4%
-0.06180903467 7
1.6%
-0.05812739687 8
1.8%
ValueCountFrequency (%)
0.1811790604 1
 
0.2%
0.1774974226 1
 
0.2%
0.1738157848 1
 
0.2%
0.1590892336 1
 
0.2%
0.151725958 1
 
0.2%
0.1406810446 1
 
0.2%
0.1333177689 1
 
0.2%
0.1222728555 2
0.5%
0.1185912177 3
0.7%
0.1038646665 1
 
0.2%

s4
Real number (ℝ)

Distinct66
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8623893 × 10-16
Minimum-0.076394504
Maximum0.18523444
Zeros0
Zeros (%)0.0%
Negative288
Negative (%)65.2%
Memory size3.6 KiB
2023-01-22T12:22:24.069545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.076394504
5-th percentile-0.076394504
Q1-0.039493383
median-0.002592262
Q30.034308859
95-th percentile0.08076737
Maximum0.18523444
Range0.26162895
Interquartile range (IQR)0.073802242

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)1.2328909 × 1014
Kurtosis0.44440167
Mean3.8623893 × 10-16
Median Absolute Deviation (MAD)0.036901121
Skewness0.73537365
Sum1.6964208 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:24.402854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03949338287 128
29.0%
-0.002592261998 108
24.4%
0.03430885888 68
15.4%
0.07120997975 33
 
7.5%
-0.07639450375 28
 
6.3%
0.1081111006 13
 
2.9%
0.1450122215 2
 
0.5%
-0.03764832683 2
 
0.5%
0.01585829844 2
 
0.5%
-0.02141183364 2
 
0.5%
Other values (56) 56
12.7%
ValueCountFrequency (%)
-0.07639450375 28
 
6.3%
-0.07085933562 1
 
0.2%
-0.06938329078 1
 
0.2%
-0.05351580881 1
 
0.2%
-0.05167075276 1
 
0.2%
-0.05056371914 1
 
0.2%
-0.05019470793 1
 
0.2%
-0.04798064068 1
 
0.2%
-0.04724261826 1
 
0.2%
-0.03949338287 128
29.0%
ValueCountFrequency (%)
0.1852344433 1
 
0.2%
0.1553445354 1
 
0.2%
0.1450122215 2
 
0.5%
0.1413221094 1
 
0.2%
0.1302517732 1
 
0.2%
0.1081111006 13
2.9%
0.09187460744 1
 
0.2%
0.08670845052 1
 
0.2%
0.08486339448 1
 
0.2%
0.08080427118 1
 
0.2%

s5
Real number (ℝ)

Distinct184
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.8280088 × 10-16
Minimum-0.12609739
Maximum0.13359898
Zeros0
Zeros (%)0.0%
Negative230
Negative (%)52.0%
Memory size3.6 KiB
2023-01-22T12:22:24.736598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.12609739
5-th percentile-0.072128455
Q1-0.033248787
median-0.0019476342
Q30.032433226
95-th percentile0.079046667
Maximum0.13359898
Range0.25969637
Interquartile range (IQR)0.065682013

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-1.2439639 × 1014
Kurtosis-0.13436583
Mean-3.8280088 × 10-16
Median Absolute Deviation (MAD)0.033140625
Skewness0.29177383
Sum-1.6964208 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:25.069148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.01811826731 11
 
2.5%
-0.03075120986 10
 
2.3%
-0.04118038519 8
 
1.8%
-0.05140053526 7
 
1.6%
-0.02595242444 7
 
1.6%
-0.03324878725 7
 
1.6%
-0.01090443585 6
 
1.4%
-0.0006092541861 6
 
1.4%
-0.06117659509 6
 
1.4%
-0.02364455757 6
 
1.4%
Other values (174) 368
83.3%
ValueCountFrequency (%)
-0.1260973856 1
 
0.2%
-0.1043648208 1
 
0.2%
-0.1016435479 1
 
0.2%
-0.09643322289 4
0.9%
-0.09393564551 1
 
0.2%
-0.08913686008 1
 
0.2%
-0.08682899322 2
0.5%
-0.08238148326 2
0.5%
-0.08023654025 1
 
0.2%
-0.07814091067 2
0.5%
ValueCountFrequency (%)
0.13359898 2
0.5%
0.1333957338 1
0.2%
0.1323726493 1
0.2%
0.1300806095 1
0.2%
0.1290194116 1
0.2%
0.120053382 1
0.2%
0.1193439942 1
0.2%
0.1063542767 1
0.2%
0.1041376114 1
0.2%
0.1032922649 1
0.2%

s6
Real number (ℝ)

Distinct56
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.4009999 × 10-16
Minimum-0.13776723
Maximum0.13561183
Zeros0
Zeros (%)0.0%
Negative224
Negative (%)50.7%
Memory size3.6 KiB
2023-01-22T12:22:25.402731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-0.13776723
5-th percentile-0.075635622
Q1-0.033179026
median-0.0010776975
Q30.027917051
95-th percentile0.081764441
Maximum0.13561183
Range0.27337906
Interquartile range (IQR)0.061096077

Descriptive statistics

Standard deviation0.047619048
Coefficient of variation (CV)-1.4001484 × 1014
Kurtosis0.23691674
Mean-3.4009999 × 10-16
Median Absolute Deviation (MAD)0.028994748
Skewness0.20791662
Sum-1.5054624 × 10-13
Variance0.0022675737
MonotonicityNot monotonic
2023-01-22T12:22:25.751963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003064409414 22
 
5.0%
0.01963283707 20
 
4.5%
0.007206516329 20
 
4.5%
-0.0010776975 19
 
4.3%
-0.01350401824 16
 
3.6%
-0.01764612516 16
 
3.6%
-0.03835665973 15
 
3.4%
-0.05492508739 14
 
3.2%
-0.005219804415 14
 
3.2%
0.01549073016 14
 
3.2%
Other values (46) 272
61.5%
ValueCountFrequency (%)
-0.1377672257 1
 
0.2%
-0.1294830119 2
 
0.5%
-0.1046303704 2
 
0.5%
-0.09634615654 2
 
0.5%
-0.09220404963 4
0.9%
-0.08806194271 2
 
0.5%
-0.0839198358 3
0.7%
-0.07977772888 4
0.9%
-0.07563562197 4
0.9%
-0.07149351505 5
1.1%
ValueCountFrequency (%)
0.1356118307 3
0.7%
0.1314697238 2
0.5%
0.1273276169 1
 
0.2%
0.119043403 2
0.5%
0.1066170823 4
0.9%
0.09833286846 2
0.5%
0.09419076154 1
 
0.2%
0.09004865463 2
0.5%
0.08590654771 4
0.9%
0.0817644408 4
0.9%

Interactions

2023-01-22T12:22:16.119447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:56.978178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:59.402344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:01.950236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:04.348499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:06.617788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:09.096542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:11.494700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:13.760500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:16.364425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:57.271610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:59.668440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:02.175371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:04.621129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:06.880927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:09.366695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:11.768938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:13.976485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:16.657439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:57.552819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:59.894931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:02.517106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:04.859848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:07.122196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:09.639588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:11.948490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:14.208952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:16.908438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:57.787606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:00.138005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:02.820872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:05.019773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:07.414418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:09.862373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:12.193810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:14.459991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:17.129854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:58.042930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:00.489573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:03.066694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:05.281778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:07.715647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:10.107434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:12.468933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:14.734001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:17.409639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:58.319747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:00.884743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:03.399886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:05.542059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:07.919091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:10.398158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:12.742900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:15.042489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:17.690408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:58.604834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:01.120178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:03.662049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:05.816412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:08.183221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:10.661930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:12.960519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:15.325713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:17.907278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:58.869367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:01.387856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:03.870181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:06.051397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:08.451052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:10.911146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:13.218687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:15.629543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:18.161970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:21:59.114870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:01.669470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:04.067512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:06.332625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:08.731560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:11.182528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:13.476803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-22T12:22:15.891922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-22T12:22:26.001631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
agebmibps1s2s3s4s5s6sex
age1.0000.2010.3510.2630.222-0.1070.2210.2650.2960.183
bmi0.2011.0000.3980.2880.295-0.3710.4590.4920.3850.213
bp0.3510.3981.0000.2750.206-0.1910.2810.3960.3810.233
s10.2630.2880.2751.0000.8790.0150.5210.5130.3320.000
s20.2220.2950.2060.8791.000-0.1970.6520.3500.2860.059
s3-0.107-0.371-0.1910.015-0.1971.000-0.790-0.450-0.2910.373
s40.2210.4590.2810.5210.652-0.7901.0000.6400.4140.348
s50.2650.4920.3960.5130.350-0.4500.6401.0000.4530.232
s60.2960.3850.3810.3320.286-0.2910.4140.4531.0000.185
sex0.1830.2130.2330.0000.0590.3730.3480.2320.1851.000

Missing values

2023-01-22T12:22:18.622450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-22T12:22:18.997946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agesexbmibps1s2s3s4s5s6
00.0380760.0506800.0616960.021872-0.044223-0.034821-0.043401-0.0025920.019908-0.017646
1-0.001882-0.044642-0.051474-0.026328-0.008449-0.0191630.074412-0.039493-0.068330-0.092204
20.0852990.0506800.044451-0.005671-0.045599-0.034194-0.032356-0.0025920.002864-0.025930
3-0.089063-0.044642-0.011595-0.0366560.0121910.024991-0.0360380.0343090.022692-0.009362
40.005383-0.044642-0.0363850.0218720.0039350.0155960.008142-0.002592-0.031991-0.046641
5-0.092695-0.044642-0.040696-0.019442-0.068991-0.0792880.041277-0.076395-0.041180-0.096346
6-0.0454720.050680-0.047163-0.015999-0.040096-0.0248000.000779-0.039493-0.062913-0.038357
70.0635040.050680-0.0018950.0666300.0906200.1089140.0228690.017703-0.0358170.003064
80.0417080.0506800.061696-0.040099-0.0139530.006202-0.028674-0.002592-0.0149560.011349
9-0.070900-0.0446420.039062-0.033214-0.012577-0.034508-0.024993-0.0025920.067736-0.013504
agesexbmibps1s2s3s4s5s6
4320.009016-0.0446420.055229-0.0056710.0575970.044719-0.0029030.0232390.0556840.106617
433-0.027310-0.044642-0.060097-0.0297710.0465890.0199800.122273-0.039493-0.051401-0.009362
4340.016281-0.0446420.0013390.0081010.0053110.0108990.030232-0.039493-0.0454210.032059
435-0.012780-0.044642-0.023451-0.040099-0.0167040.004636-0.017629-0.002592-0.038459-0.038357
436-0.056370-0.044642-0.074108-0.050428-0.024960-0.0470340.092820-0.076395-0.061177-0.046641
4370.0417080.0506800.0196620.059744-0.005697-0.002566-0.028674-0.0025920.0311930.007207
438-0.0055150.050680-0.015906-0.0676420.0493410.079165-0.0286740.034309-0.0181180.044485
4390.0417080.050680-0.0159060.017282-0.037344-0.013840-0.024993-0.011080-0.0468790.015491
440-0.045472-0.0446420.0390620.0012150.0163180.015283-0.0286740.0265600.044528-0.025930
441-0.045472-0.044642-0.073030-0.0814140.0837400.0278090.173816-0.039493-0.0042200.003064